Headwater sediment dynamics in a debris flow

Full text

Turn on search term navigation

Earth Surf. Dynam., 4, 489513, 2016 www.earth-surf-dynam.net/4/489/2016/ doi:10.5194/esurf-4-489-2016 Author(s) 2016. CC Attribution 3.0 License.

Alexandre Loye1, Michel Jaboyedoff1, Joshua Isaac Theule2, and Frdric Libault2

1Risk-group ISTE Institute of Earth Sciences, University of Lausanne, Lausanne, Switzerland

2Universit Grenoble Alpes, Irstea, UR ETNA, Saint-Martin-dHres, France

Correspondence to: Alexandre Loye ([email protected])

Received: 1 December 2015 Published in Earth Surf. Dynam. Discuss.: 14 January 2016 Revised: 19 May 2016 Accepted: 20 May 2016 Published: 28 June 2016

Abstract. Debris ows have been recognized to be linked to the amounts of material temporarily stored in torrent channels. Hence, sediment supply and storage changes from low-order channels of the Manival catchment, a small tributary valley with an active torrent system located exclusively in sedimentary rocks of the Chartreuse Massif (French Alps), were surveyed periodically for 16 months using terrestrial laser scanning (TLS) to study the coupling between sediment dynamics and torrent responses in terms of debris ow events, which occurred twice during the monitoring period. Sediment transfer in the main torrent was monitored with cross-section surveys. Sediment budgets were generated seasonally using sequential TLS data differencing and morphological extrapolations. Debris production depends strongly on rockfall occurring during the winterearly spring season, following a power law distribution for volumes of rockfall events above 0.1 m3, while hillslope sediment reworking dominates debris recharge in spring and autumn, which shows effective hillslopechannel coupling. The occurrence of both debris ow events that occurred during the monitoring was linked to recharge from previous debris pulses coming from the hillside and from bedload transfer. Headwater debris sources display an ambiguous behaviour in sediment transfer: low geomorphic activity occurred in the production zone, despite rainstorms inducing debris ows in the torrent; still, a general reactivation of sediment transport in headwater channels was observed in autumn without new debris supply, suggesting that the stored debris was not exhausted. The seasonal cycle of sediment yield seems to depend not only on debris supply and runoff (ow capacity) but also on geomorphic conditions that destabilize remnant debris stocks. This study shows that monitoring the changes within a torrents in-channel storage and its debris supply can improve knowledge on recharge thresholds leading to debris ow.

1 Introduction

In steep mountain catchments, rainfall intensity and duration (including snowmelt) are insufcient to predict debris ow occurrence, even though the initiation of runoff-generated debris ows requires signicant water inow (Van Dine, 1985; Decaulne and Saemundsson, 2007; Guzzetti, 2008). In many cases, the properties of the channel reach which determine the amount of debris that can be entrained can be often more important than the mechanisms of initiation induced by the hydrological or meteorological conditions prior

Headwater sediment dynamics in a debris ow catchment constrained by high-resolution topographic surveys

to the event (Hungr, 2011; Theule et al., 2015). The frequency and magnitude of debris ow have been recognized to be linked to the amount of material temporarily stored in channel reaches (Van Steijn et al., 1996; Cannon et al., 2003; Hungr et al., 2005), such that hillside sediment delivery, recharging those channels, represents a key factor for the occurrence of debris ows (e.g. Benda and Dunne, 1997; Bovis and Jakob, 1999; Berti et al., 2000). This implies efcient hillslopechannel coupling (Hooke, 2003; Schlunegger et al., 2009; Johnson et al., 2010). Therefore, the rate of sediment supply needs to be considered for predicting debris ow haz-

Published by Copernicus Publications on behalf of the European Geosciences Union.

490 A. Loye et al.: Headwater sediment dynamics

Chambery

Production zone

Zone of transfer

Figure 1. Inset: map of the study area; the Manival catchment is in solid red and the impressive debris fan is hatched. Main: aerial view of the Manival catchment, draped over a topographic model; sediment supply is concentrated in the headwater (production zone) as erosion activity from the middle and lower catchment is not connected to the torrent (zone of transfer) (image: Aerodata International Surveys; DEM: Irstea UR ETNA).

ards (Rickenmann, 1999; Jakob et al., 2005). However, the difculty results in quantifying sediment processes and rates and volumes from hillslopes and in-channel debris storage (Peiry, 1990; Zimmermann et al., 1997).

The quantication of the overall sediment production and transfer rate has increasingly relied upon multi-temporal digital stereophotogrammetry (Coe et al., 1993; Chandler andBrunsden, 1995; Veyrat-Chavillon and Memier, 2006) and elevation difference from high-resolution digital elevation models (HRDEMs) (Smith et al., 2000; Wu and Cheng, 2005; Roering et al., 2009; Theule et al., 2012). In terrain dominated by steep slopes, traditional aerial-derived digital elevation models (DEMs) are typically inappropriate to study geomorphic processes. Limitations include the poor rendering of small topographic changes (Perroy et al., 2010), the poor representation of steep terrain with small curvature radii and data gaps in vertically oriented and overhanging topography. Even on gentler gradients, the sharp breaks in slope, encountered in erosion scars for instance, are often insuf-

ciently modelled by airborne HRDEMs, leading to erroneous volume estimations (Bremer and Sass, 2011). This represents a serious drawback in estimating the sediment budget of steep terrain, where sediment activity comes mostly from rock walls and rugged gullies. Because of these issues, many hillslope and rock slope process studies have used terrestrial laser scanner (TLS) data to build the topographic model (Jaboyedoff et al., 2012). The recent development of long-range TLS devices provides an effective means of acquiring high-resolution topographic information that can adequately reect the morphology of steep bedrock-dominated areas.The practical disadvantages in data acquisition inevitably related to ground surveys can be compensated for by exibility in transport, ensuring a full coverage with minimal zones of topographic shadowing.

This paper presents a quantitative study of sediment recharge and channel response leading to debris ow events, using 3-D digital terrain models acquired by TLS. This is illustrated on the Manival (French Alps), a torrent that experi-

Earth Surf. Dynam., 4, 489513, 2016 www.earth-surf-dynam.net/4/489/2016/

Sediment trap

A. Loye et al.: Headwater sediment dynamics 491

Figure 2. Geological map of the catchment headwater (production zone) after Gidon (1991) and location of rst-order debris ow channels (thick blue line) and their respective watersheds (white lines). For the ease of analysis, the Roche Ravine and Col du Baure subcatchments in the east side were further subdivided according to their gully complex (dotted white lines).

ences runoff-generated debris ow almost every year (Pteuil et al., 2008). The surveys captured hillslope processes and sediment dynamics occurring throughout the system including the tributary channels down to the main torrent and were performed periodically over 16 months. The spatio-temporal variability of debris production and subsequent transport and storage of sediment are analysed on a seasonal timescale, in order to discuss the debris supply dynamics and the implications in debris ow initiation. This study also complements a parallel investigation regarding the controls on debris ow erosion and bedload transport in the Manival torrent (Theule et al., 2015).

2 Study site

2.1 General setting

The 3.9 km2 Manival catchment located at the edge of the

Chartreuse Massif (France) (Fig. 1) has a rugged, 1200 m relief watershed, resulting from deep headward entrenchment (Gidon, 1991). The topography consists of a narrowly conned head and a steep-sided colluvium-lled valley, delimited in the west by a series of rock walls and screemantled deposits separated by rock couloirs and in the east by steep rock and talus slopes divided by gullies. The lithology ranges in age from Late Jurassic to Early Cretaceous (Fig. 2) (Charollais et al., 1986). In the heart of the basin, thick sequences of calcareous marl interbedded with layers of marl predominate. Towards the ridge, the bedrock evolves progressively from stratied to more massive limestone. The

valley sides are formed by the fold limbs of an anticline, where secondary folding and minor faults induce local variations in structure (Gidon, 1991). This tectonic setting and the varying stratigraphic competence have strongly inuenced the topographic development of the catchment, providing a dynamic geomorphic environment producing considerable runoff as a response to heavy, frequent rainstorms (Fig. 3).

2.2 Characteristics of the headwater sediment dynamics

The contemporary geomorphic activity contributing to the torrents recharge with debris is concentrated exclusively in the headwater, where no remnant glacial deposits are found (Gruffaz, 1997). In the upper catchment, large old rock deposits ooring the west side hillslope (Fig. 4) have dramatically inuenced the bottom topography, and thus the channel network, resulting in a conjunction of four rst-order debris ow channels deeply incised down to the bedrock in several reaches. The upper catchment can therefore be subdivided into ve subcatchments in terms of sediment recharge (Fig. 2). Bed entrenchment is now constrained by check dams. However, lateral erosion still occurs episodically by ooding and debris ow scouring.

The style of sediment production and delivery is somewhat different throughout the headwater, according to the local morphology and the lithologic and structural setting. The major geomorphic processes, identied preliminarily by observations from aerial photographs and eld investigations, were initially characterized in a map (Fig. 4) describing the spatial distribution of geomorphic features and sediment

www.earth-surf-dynam.net/4/489/2016/ Earth Surf. Dynam., 4, 489513, 2016

492 A. Loye et al.: Headwater sediment dynamics

No data (winter snow and ice)

Debris Flow 01

Debris Flow 02

Rainfall Intensity (mm h )

-1

Bedload

transport

No data

Apr 09 May 09 Jun 09 Jul 09 Aug 09 Sep 09 Oct 09 Nov 09 Dec 09 Apr 10 May 10 Jun 10 Jul 10 Aug 10 Sep 10

Aug 09 Sep 09 Oct 09 No 09 Jun 10 Ju 10 Aug 10 Sep 10

...

Figure 3. Maximum rainfall intensity over the monitoring period measured by a rain gauge located at the top of the torrent (see Fig. 4) and calculated for a 5 min time interval. The mean annual precipitation is about 1500 mm in the headwater of the Manival (modied from Loye, 2013).

transfer processes contributing to debris recharge in the rst-order channels. The west and upper sides are dominated by rockfall. Large rock collapses delimited by persistent joints occur due to the progressive degradation of the slope underneath (Loye et al., 2011). Where the slope gradient allows scree and soil development, erosion scars can be observed; sediment sources are remobilized from discrete shallow landslides. Depending on the location and size, rockfall can reach the channels directly or accumulate on slopes or in ravines, before being subsequently routed to high-order segments by a combination of gravitational and hydrological processes.Towards the east, the erosion seems to be more progressive through the formation of gullies (Loye et al., 2012). Near the ridge, the slopes display mostly talus and scree deposits lightly covered with vegetation, whereas the hillside below exposes steepened rock slopes. Many active erosion scars can be observed. They contribute debris into gullies and talus slope deposits that are subsequently entrained in channels downslope.

Historical records of debris ows since the 18th century show a frequency of 0.3 events per year that reached the apex of the fan (Brochot et al., 2000). The largest event deposited approximately 60 000 m3. However, the torrent experiences smaller uxes of debris (< 1000 m3) usually not reported in archives. Such events can occur 23 times per year, when initiated by intense runoff (Veyrat-Charvillon, 2005). Volumes of debris deposited in the sediment trap for the last 25 years are on average 2200 m3 yr1, reaching a maximum of 7000 m3 yr1 in 2008 (RTM service, National Forests Ofce (France)).

3 Methods and data processing

3.1 Topographic monitoring using TLS

The terrain was surveyed with an ILRIS-3D laser scanner (Optech Inc.). This device provides a range of up to 1.2 km for 80 % reectivity surface, and the instrumental precision

Table 1. Dates of TLS acquisitions. Note that for the analysis, the second survey was merged with the rst one (see text for details).

Monitoring Start and end dates Period ID period (MP) of survey

First 01/04/200912/07/2009 MP1Second 12/07/200930/08/2009 merged with MP1 Third 30/08/200911/11/2009 MP2Fourth 11/11/2009 08/07/2010 MP3

is about 7 mm/100 m range for both distance and position (Optech Inc.). The overall coverage of the upper catchment with TLS point clouds required 50 scans using a 20 % surface overlap. These scans were collected over a 5-day period from nine individual viewpoints to ensure a full 3-D rendering of the topography. Particular attention was given to irregular regions and major breaks in slope, such as rock couloirs and deep-cut gullies. Using multiple scanning locations allowed us to limit shadow zones and increase the point cloud density of the scanned area. A series of four surveys was performed for each season during 2009, and one extra survey was performed in July 2010 to analyse the effect of the preceding winter period (Table 1). The monitoring setup remained similar for all surveys. Post-processing of the TLS raw data was done using Polyworks (InnovMetric). Erroneous points and vegetation were ltered manually, ensuring a total control of the removed data to preserve a high density of points in topographic features with small radii curvature. Although this procedure is time consuming, box (semi-)automatic approaches to lter vegetation accurately still remain in a stage of development for dissected mountain morphology (Brodu and Lague, 2012). Each of the multiple scans of a survey was merged with another one using common tie points of permanent topographic features and the dataset was processed as 12 standalone sub-datasets, rather than all processed together.Given the size of the monitored area, dividing the point cloud

Earth Surf. Dynam., 4, 489513, 2016 www.earth-surf-dynam.net/4/489/2016/

A. Loye et al.: Headwater sediment dynamics 493

0 125 250m

Scree-mantled hillslope

Bedrock slopes

Debris flow

Rock couloirs / scree hollow

Rock slopes

Cliffs (shaded relief)

Covered with forest

Debris flow deposit

Covered with vegetation

Scouring / bank erosion

Uncovered

1st order

debris flow channel

Debris deposit (active)

Rock collapse deposits

Debris slide (fresh)

Erosion scar

Older to ancient

Active or dormant

Fresh

Rain gauge

Figure 4. Geomorphic process map (contour interval: 20 m) illustrating the spatial pattern of sediment sources and transfer in the rst-order channel complex. Note the impressive rock collapse deposits now crossed by four rst-order debris channels. Their bed incision is strongly constrained by a series of check dams (marked as black T on the map), but erosion scars all along the deposit suggest that the reaches are still subject to lateral erosion.

into smaller datasets avoids the propagation of inaccuracy through large co-registered scan series. ICP (iterative closest point) algorithms (Besl and McKay, 1992), which minimize the distance between two point clouds, were used to determine the best alignment of subsets surveyed at different times in order to obtain the best co-registration within a time series. The same procedure was applied between subset point clouds and a point cloud derived from a commercial airborne laser scanner (mean density: 6.9 pts m2) and acquired in June 2009 to place the TLS data into the standard

Lambert projection coordinate system used in France. The initial survey point cloud data were set as the surface model of reference. Each successive survey was georeferenced onto this reference using ICP. The topographic change occurring between two successive surveys is too localized to inuence the global co-registration within two survey data subsets consisting of millions of data points, hence the alignment accuracy. More details about multiple scan registration techniques and point cloud time series comparison can be found in Oppikofer (2009). The generated surface produced by the above

www.earth-surf-dynam.net/4/489/2016/ Earth Surf. Dynam., 4, 489513, 2016

494 A. Loye et al.: Headwater sediment dynamics

Table 2. TLS data and surface coverage characteristics of the ve subcatchments from the rst monitoring period (MP1). As the view points and parameters of acquiring remained similar, the values are essentially the same for all surveys.

Surface Lidar data survey Scanned area

Subcatchment Total Vegetation Number Mean Mean Mean Total Percentage of the non-name (km2) cover (%) of points spacing (m) range (m) density (pts m2) (km2) vegetated surface

Col du Baure 0.29 43.0 37 625 236 0.055 131 340 0.11 84 Roche Ravine 0.30 20.5 43 736 412 0.071 278 251 0.17 79 Manival 0.35 9.1 40 192 976 0.096 349 141 0.28 90 Grosse Pierre 0.08 9.0 9 703 449 0.110 447 145 0.07 97 Genivre 0.35 26.6 19 886 472 0.108 311 109 0.18 79

Production zone 1.36 22.7 151 144 545 0.081 275 219 0.82 84

Topographic surface area.

procedure has a point spacing ranging from 2.5 to 18 cm according to the distance of acquisition. A maximum range of about 800 m was reached on the top peak of the catchment with a point cloud density of 25 pts m2. The surface coverage of our surveys represents 84 % of the deforested area under investigation (Table 2).

3.2 Topographic change identication and characterization

The active geomorphic features within two successive datasets were identied on a point-by-point basis using the short-distance neighbouring point search algorithm (Bitelli et al., 2004) that computes, in 3-D, the shortest difference vectors between the points of two datasets. The vector sign indicates the net change direction of topography, i.e. surface of erosion or deposition. A set of points (cluster) was considered active if at least eight adjacent points of similar sign displayed an absolute difference above the limit of detection (LoD, see Sect. 3.4). Each active feature was outlined visually using the point cloud of difference (Fig. 5a). The point clusters of both survey datasets, which correspond to the topography of the active features, were extracted according to their spatial extend coordinates and each detected geomorphic feature was labelled as follows:

1. rock slope erosion, characterized by rockfall or rock-slides;

2. hillslope erosion, specically the reworking of loose or compacted debris on slope, in gullies, and in channels;

3. deposition, including material aggradation initiated by both rock slope failure (new production) and remobilization of debris.

Using the images captured by the TLS integrated camera, clusters of points not corresponding to geomorphic process activity, such as snowmelt, were ignored.

3.3 Volume computation of each geomorphic feature

As the volume of active features cannot be directly computed by differencing TLS point datasets, the active features of two successive point clouds must be interpolated into continuous surfaces (DEM). Gridded model (or raster) is regarded as being the most effective type of model to use for irregularly distributed datasets, which sometimes contain few or no points (El-Sheimy et al., 2005), as can be the case for rockfall and erosion scars. The algorithm chosen for the interpolation of the DEM has little inuence on the nal result, as TLS data provide an extremely dense coverage of the detected objects (Anderson et al., 2005). Therefore, they were interpolated using linear inverse distance weighting (Burrough and McDonnell, 1998) and generated in a regular grid separately. The grid spacing and direction of interpolation were designed in a specic way for each feature: the coordinate system of reference was replaced by a local orthogonal system where the xy axes represent the average plane of topography nearby (Fig. 5b). This new reference frame was dened using eigenvalue decomposition of the covariance matrix of the point cloud of reference (Shaw, 2003). Interpolating the surface elevation in the direction of local topography allows the generation of a realistic DEM independent of slope steepness and, thus, a close realistic representation of topography in the case of overhanging features. The cell size was dened according to the point spacing distribution of both datasets. A series of tests revealed that setting the grid spacing at 68 % of the cumulative frequency distribution of point spacing provides a continuous surface reconstruction while keeping a high degree of detail from the point cloud. This ensures an accurate volume computation of geomorphic features. The volume was computed as the sum of the cell difference in elevation (both positive and negative) between the successive DEMs. Absolute cell differences lying below a given threshold (see Sect. 3.4) were not considered. This volume computation using a local deterministic method of interpolation and an adaptive gridding approach was developed in the Matlab numerical computing environment.

Earth Surf. Dynam., 4, 489513, 2016 www.earth-surf-dynam.net/4/489/2016/

A. Loye et al.: Headwater sediment dynamics 495

(a)

(a0

5.00 m

0.350.300.250.200.150.100.050.00 -0.05 -0.10 -0.15 -0.20 -0.25 -0.30 -0.35 -5.00 m

LoD

Scene

Hillslope erosion

Rockfall

TLS dataset

Scale : wide = 200 m

height = 300 m

Point cloud of difference

Dataset 1 Dataset 2

(b)

Active feature detection on the sequential point clouds using 3-D point clouds of difference

Data point extraction of the pre- and post-topography

of the feature

z Local coordinate system

Global coordinate system

z y

y x

Coordinate system transformation into the average orientation of the slope

Projection of the datasets into the local coordinates system +Regular grid interpolation using IDW

- 0.005

- 0.01

- 0.015

Volume computation=sum of the cell difference in elevation between the successive DEMs

Figure 5. 3-D detection (a) and schematic illustration (b) of the extraction and volume computation method of an individual active feature provided by two successive point cloud datasets.

www.earth-surf-dynam.net/4/489/2016/ Earth Surf. Dynam., 4, 489513, 2016

496 A. Loye et al.: Headwater sediment dynamics

Histogram statistics ICP : Georeferencing point cloud April 2009July 2010

Subcatch. name Manival

Mean ( ) 0.000104

SD ( ) 0.060718

Minimum (min) -0.212658

Maximum (max) 0.212658

Peak 48593

Number of Points 3526850

min

max

Figure 6. Distribution of the distance between two survey point clouds after the process of georeferencing using the ICP procedure. The distance approaches normal distribution with a zero mean, showing that errors generated by multiple scan registration and point cloud survey georeferencing are Gaussian, random, and independent. Data are given in metres.

Table 3. Registration and georeferencing standard deviations (in centimetres) of the position uncertainty on a point by point basis that was used to derive the LoD at 95 % condence interval and subsequently to detect topographic changes down to a certain minimum volume of geomorphic features.

Sub- 2 co-registered 2 co-georeferencing 2 Taylor uncertainty catchment (cm) (LoD) (cm) (cm)

name dreg =

q2dPC1 + 2 dPC2

[parenrightbig]

Survey Monitoring period Monitoring period

First Second Third Fourth First Second Third First Second Third

Col du Baure 1.9 1.7 1.5 1.5 5.9 6.9 6.9 5.1 4.5 4.2 Roche Ravine 3.2 2.9 2.6 2.7 8.4 9.4 9.0 8.6 7.7 7.5 Manival 4.6 4.1 3.0 3.4 9.6 10.2 12.2 12.3 10.2 9.1 Grosse Pierre 4.1 3.0 3.3 3.3 10.6 10.6 12.2 10.2 8.9 9.3 Genivre 3.7 3.6 3.2 3.6 6.7 7.6 8.3 10.3 9.6 9.6

PC: point cloud used to generate the map (point cloud) of difference in 3-D.

3.4 Point cloud accuracy and limits of detection of the geomorphic features

A reliable identication of erosion and deposition features requires the denition of a LoD, where the change in elevation between successive point clouds can be considered real as opposed to noise. Each TLS data point theoretically has a unique precision depending on the range and laser incidence angle (Buckley et al., 2008). In practice, the individual point precision of a scan can be assumed to model a surface with a global uniform uncertainty, considering the very high point density (Abelln et al., 2009). Given the homogeneity of surface error and considering that the distance between sequential points at a position (x, y) should tend to 0, the accuracy of TLS data can be estimated by substituting the precision of each data point by a singular measurement of the error associated with the entire point distribution across the surface (Lane et al., 2003). Hence, the uncertainty related to both scan registration and point cloud georeferencing, the instru-

mental error included, was dened by the standard deviation of the distance (d) between the points (Fig. 6). The LoD was therefore set at 2 of the co-georeferencing and corresponds to the 95 % condence limit (Table 3). Comparison with the approach considering the error propagation for all uncertainties associated with each point cloud and assuming a normal distribution of the error in distance (Taylor, 1997) shows that the uncertainties considered here are consistent.

In the case of volume computation, information on elevation uncertainty associated with each point cloud survey needs to be extended to the DEM on a cell-by-cell basis. For any grid cell (i, j) generated by the interpolation of adjacent points p with independent elevation, the uncertainty of a cell elevation can be considered the standard deviation (e) of the data points elevation, where ei,j = ep/pn according to the

equation of standard error of the mean, n being the number of points to dene the cell elevation. The elevation uncertainty

Earth Surf. Dynam., 4, 489513, 2016 www.earth-surf-dynam.net/4/489/2016/

A. Loye et al.: Headwater sediment dynamics 497

for each cell in a DEM of difference is then expressed by

[Delta1]ei,j =

[radicalBig]

1i, j

2. (1)

The volume uncertainty is then calculated by summing up the derived volume uncertainty of each cell of the feature as follows:

[Delta1]vfeature = a

2ei,j

[radicaltp]

[radicalvertex]

[radicalbt]

Xi=1n

Xj=1

[Delta1]ei,j

2 5, (2)

with a = cell area. The smallest detectable volume is about

103 m3 (10 10 10 cm) (Table 3) but can reach up to

0.006 m3 (25 25 10 cm) depending on the point spacing

at maximum range. Topographic change detection and volume computation accuracy depend not only on the quality of the TLS data, such as point density and post-processing-related inaccuracy. They also depend on the complexity of the surface geometry, like in our case, by integrating the range in position of all data points dening each grid cell value of a feature. Monitoring the hillslope activity is also limited by the ability of the process to create a distinct topographic change. Consequently, the deposition of individual small rockfalls was not always detected, as detached rock masses fragment into smaller pieces that are below the LoD. A similar issue was observed for erosion processes within debris. Nevertheless, most of the material accumulation could be related to upslope landslides or scouring. The sediment budgets were therefore kept in volumetric units, as they are commensurate for a consistent analysis. They were not converted to mass, although this would make more sense for comparing hillslope processes and rock slope yields. Such conversion requires an accurate density value of each surface process, whose approximations introduce additional unknowns. Deposition related to rock failures may therefore be slightly overrepresented in the sediment balance, although this could be partly compensated for by a limited detection of small features.

3.5 Sediment budgets of the Manival torrent

Monitoring of the coarse-sediment transfer has been performed all along the main torrent channel to the sediment trap located downstream on the alluvial fan. The in-channel storage change was established after every noticeable ow event, using the morphological approach based on cross-section survey techniques (Ashore and Church, 1998), and the volume of sediment deposited in the sediment trap was measured by TLS survey differencing. Sequential volumes of recharge enable us to study the inuence of debris supply from the production zone through the seasons. The characteristics and observational analysis of this event-based monitoring were documented in detail in Theule et al. (2012, 2015) and are therefore not described any further.

3.6 Estimation of debris production rate

A rate of debris production for the study period is obtained from the total volume of rock slope erosion. An objective estimation can be deduced by characterizing the cumulative distribution of rockfall volumes with a power law as follows (Gardner, 1970):

N(v > V ) = aV b. (3)

N is the rockfall frequency for a volume greater than V and a and b are constants. a depends on the study size and on rock slope properties, whereas b tends to be rather site independent (Dussauge-Peisser et al., 2002; Dewez et al., 2011). Considering that rock slope process activity causing rockfall does not uctuate much over time, the inventory analysis can be used to infer the frequency of the occurrence of larger events. This is done by integrating the rockfall frequency derivative n(v) = dNdV over the range of potential volumes.

The estimation of the total volume Vt per unit time that can be expected on average over a longer period of observation is therefore expressed by (modied from Hantz et al., 2002)

Vt =

n(Vmax)

[integraldisplay]

n(Vmin)

V dn = ab

Vmax

[integraldisplay]

V V b1dV =

Vmin

Vmax

[integraldisplay]

V bdV =

ab (1 b)

V 1b

[vextendsingle][vextendsingle][vextendsingle][vextendsingle][vextendsingle][vextendsingle][vextendsingle][vextendsingle]

Vmin

Vmax

. (4)

The goodness of t of the power law was evaluated with the [notdef]2 test (Taylor, 1997) and the standard deviation of values a and b was determined with the maximum likelihood estimate (Aki, 1965). The erosion rates are assessed by dividing Vt with the surface prone to rockfall.

4 Results: hillslope process activity monitoring

4.1 First monitoring period (AprilAugust 2009)

The topographic changes recorded from July to August 2009 did not show any relevant geomorphic activity (only a few small rockfalls). These results were therefore merged with the preceding monitoring period.

Rock slope activity is dominated by individual small rock-falls distributed throughout the upper catchment. Only few events exceed 1 m3, such that contributions in terms of debris production are marginal in most parts of the catchment (Fig. 7). The most signicant geomorphic activity was located almost exclusively in the major gullies of Baure and Grosse Pierre ravines and consists essentially of debris scouring of a few 100 m3 redeposited further down. Material reentrainments were also observed in several other smaller gullies, but their volumes are relatively small. The rock couloirs of the Genivre subcatchment and the scar of the old rock

www.earth-surf-dynam.net/4/489/2016/ Earth Surf. Dynam., 4, 489513, 2016

Vmin

498 A. Loye et al.: Headwater sediment dynamics

deposit barely showed any geomorphic activity. The channels displayed a net incision (636 m3 43) in the upper reaches.

Bedload aggradation remains very low (+90 m3 6). Below

the upper conuence, the channel trunk exhibits a mixed pattern of zones of erosion (60 m3 2), such as gravel-wedge

scouring, and zones of redeposition of entrained material (+80 m3 4) induced by bedload transport.

4.2 Second monitoring period (SeptemberNovember 2009)

Rock slope activity remains similar in spatial extent and volumes to the previous survey period, but rockfall frequency is higher (Fig. 8). Hillslope process activity was more widespread on the east side but more localized on the western valley walls, while the rock couloirs showed no geomorphic activity. In the upper headwater, material reworking was concentrated almost exclusively in the steep tributary gullies. They displayed scouring of a relatively large volume (357 m3 12). Deposition features along the thal

weg were almost inexistent (+18 m3 1.3). In the south

east, not only the Baure Ravine (net erosion: 61 m3 8)

but the whole series of hillside gullies exhibited signs of activity, such as erosional segments alternating with deposition.On scree slopes, several minor areas with erosional rills and their associated debris deposits were observed, some of them reaching the channel trunk (+42 m3 2). Such small hillside

debris ows were probably triggered by sediment entrainments within the rills, as no evidence of sliding at their head was observed. The channels show a net erosion upstream (482 m3 18), whereas continuous incisions were more

pronounced in the Manival channel (443 m3 16) and also

in the Roche Ravine (40 m3 3). Deposition zones were

almost completely absent (15 m3 1.3). Towards the up

per conuence, the lower segments of the Manival channel exhibited continuous zones of aggradation (97 m3 6) that

were scoured on one side. This morphology is characteristic of closed-process debris ow levees and run-up zones beside the incised channel bed. Below the upper conuence, channel bed cut (40 m3 2) and ll (+16 m3 1) was sparse

and concentrated at the junction with hillside gullies. Such a pattern of bed reworking demonstrates the connectivity of the Baure gully series with the channel trunk.

4.3 Third monitoring period (NovemberJuly 2010)

This period showed an important increase in rock slope erosion, both in frequency and magnitude, resulting from the occurrence of large slope failures and enhanced localized rockfall activity, for instance in rock walls made of calcareous marl situated directly above the Manival (2035 m3 39)

and the Roche Ravine (256 m3 17) channels (Fig. 9). Most

of the debris collapses supplied the channel directly; the rest was temporarily deposited in breaks in the slope. The lower headwater part showed a great uctuation as well

(Genivre: 116 m3; Grosse Pierre: 145 m3). At the top of the Baure Ravine, 816 m3 25 of rock fragments contributed

substantially to recharge the sediment storage at the gully head. Below, debris inlling was continuously scoured. A 1170 m3 18 rockslide is responsible for a large channel

inll in the Manival subcatchment. Several other smaller rockfalls contributed to the recharge of tributary gullies and scree hollows. In the Roche Ravine, debris deposits were sparse because rockfall remained of a low magnitude on average (571 events < 1 m3), although frequency was high (578 events). The large debris inll at the channel head was caused by two erosion scars in the gullies (270 m3 14 and

65 m3 4). In the rock couloirs of the Genivre subcatch

ment, a signicant accumulation of material from landslides and rockfalls was observed (remnant volume: 204 m3 13),

taking into account that the hillslope erosion represents 450 m3 (14). In the Grosse Pierre Ravine, 343 m3 17 of

debris were accumulated at the rock couloir outlet, recharging the scree slope above the channel head. In the Col du Baure, relatively large aggradation in the lower part of tributary gullies was observed (remnant volume: +142 m3 2),

resulting from material entrainment. Several debris slides were also detected on scree slopes, without any contact with the channel trunk.

The upper channel reaches were clearly depositional, as a consequence of large slope failures. The Manival channel showed a continuous zone of remnant accumulation of 948 m3 (18) of which a portion was carried along down

stream as bedload. Towards the conuence, erosion dominated (487 m3 19) over deposition (+25 m3 3). In the

Roche Ravine, a continuous zone of erosion in the scar of the old rock deposit produced debris accumulation mostly on the slope. But a landslide of 190 m3 9 reached the channel.

Overall, aggradation was observed all along the channel head (+148 m3 18) and scouring was limited (65 m3 4).

From the conuence downstream, the channel behaviour is dominantly erosional (97 m3 4) almost without any

aggradation (+3 0.3 m3).

4.4 Rock slope production inventory

Over the 16 months, 1866 rockfalls with volumes ranging from 104 to 103 were recorded. This yields a total of 3575 m3 30 and an erosion rate of 3.1 mm yr1, given the

topographic surface area of rock faces. The inventory follows a power law (Fig. 10) with a 99 % condence level for events larger than 3 m3 ([notdef]2 value = 17.3). For events larger than

1 m3, the power law is accepted at the 95 % condence level ([notdef]2 value = 5.89). Both threshold volumes provide a b value

close to 0.81 0.06. Considering only the volumes above

10 m3 (25 events) gives a b value of 0.76. Below 0.1 m3, the observed frequency deviated clearly from the power law regime until the rollover reached an approximately constant rate for the smallest volumes. According to our inventory, rockfall of more than 1 m3 is expected 153 11 times per

Earth Surf. Dynam., 4, 489513, 2016 www.earth-surf-dynam.net/4/489/2016/

A. Loye et al.: Headwater sediment dynamics 499

1 monitoring period

Rock slope erosion Volume (m3)

LoD - 0.250.25 - 0.750.75 - 5.05.0 - 23 23 - 25

Deposition Volume (m3)

LoD - 2.02.0 - 5.05.0 - 15 15 - 60 60 - 270

Hillslope erosion Volume (m3)

LoD - 5 5 - 2020 - 50 50 - 200 200 - 220

No lidar coverage

April 2009September 2009

0 250

125 Metres

Figure 7. Geomorphic activity revealed by comparing the topographic differences of the two successive TLS surveys operated in April and August 2009. The sediment budgets for each subcatchment are detailed in Fig. 13.

2 nd monitoring period SeptemberNovember 2009

Rock slope erosion Volume (m3)

LoD - 0.150.15 - 0.500.50 - 1.001.00 - 4.004.00 - 6.00

Deposition Volume (m3)

LoD - 0.250.25 - 2.02.0 - 5.05.0 - 15 15 - 25

Hillslope erosion Volume (m3)

LoD - 3.03.0 - 10 10 - 30 30 - 50 50 - 75

No lidar coverage

0 250

125 Metres

Figure 8. Geomorphic activity revealed by comparing the topographic differences of the two successive TLS surveys operated in August and November 2009. The sediment budgets for each subcatchment are detailed in Fig. 14.

www.earth-surf-dynam.net/4/489/2016/ Earth Surf. Dynam., 4, 489513, 2016

500 A. Loye et al.: Headwater sediment dynamics

3 monitoring period

Rock slope erosion

[m 3]

November 2009July 2010

Volume (m3)

LoD - 10 10 - 6060 - 300 300 - 700 700 - 1100

Deposition Volume (m3)

LoD - 25 25 - 100 100 - 200 200 - 600 600 - 1200

Hillslope erosion Volume (m3)

LoD

5 5 - 1515 - 60 60 - 180 180 - 270

No lidar coverage

0 250

125 Metres

Figure 9. Geomorphic activity revealed by comparing the topographic differences of the two successive TLS surveys operated in November 2009 and July 2010. The sediment budgets for each subcatchment are detailed in Fig. 15.

Table 4. Sediment budget (in cubic metres) of the Manival torrent established after noticeable events using the morphological approach after Theule et al. (2012). The torrent recharge (sediment input) is estimated from in-storage changes in channels and volumes deposited in the sediment trap (output).

Monitoring Survey dates in the torrent Sediment Storage Channel Channel Sediment Total period output change erosion deposition input sediment Input

First no. 1 06/07/200928/08/2009 1873 62 2034 559 5232 136 3199 63 063 063 Second no. 2 30/08/200907/10/2009 0 789 84 1409 31 2197 53 736842 9341102

no. 3 08/10/200912/11/2009 302 36 73 66 1546 36 1473 31 198260Third no. 4 13/11/200901/06/2010 580 45 580 81 1961 45 1372 36 036 174844

no. 5 02/06/201008/06/2010 3320 176 3052 272 7658 178 4606 93 0537

no. 6 09/06/201008/10/2010 819 46 608 82 2246 46 1637 36 174246

The TLS survey of the third monitoring period (MP3) lasted until 08/07/2010; no. 6 was not considered for the analysis of the sediment budgets.

year on average. The largest event (1170 m3) occurs every 2 years, and the 1-year return period rockfall has a volume of approximately 465 m3. Considering only these classes of volumes of the inventory (see Table 6), the rock slope production reaches a rate of 3678 m3 yr1 210 (4 mm yr1 0.3).

4.5 Torrent in-channel storage changes

Two debris ows with multiple surges and several remarkable bedload transport events were observed in the main torrent during the survey period (Theule et al., 2012). A debris ow occurred on the 25 August 2009, caused by a short-

duration rainstorm. The volume of sediment eroded in the torrent (5232 m3 136) is equivalent to the volume that was

redeposited in both the torrent itself and the sediment trap (5072 m3 125), suggesting that the majority of entrained

material was stored in the torrent (Table 4). Sediment input from the headwater can be considered marginal. Before that, no signicant torrent activity was observed, despite a series of rainfall events with low to moderate intensity. In September 2009, a long period of moderate rainfall intensity caused material reworking by bedload transport all along the torrent.However, no sediment was supplied to the sediment trap. A

Earth Surf. Dynam., 4, 489513, 2016 www.earth-surf-dynam.net/4/489/2016/

A. Loye et al.: Headwater sediment dynamics 501

(a)

Cumul. volume distribution

Fitted power law

(b)

Cumul. volume distribution

Fitted power law

Normalized annual frequency [number year ]

a: 34.1

b: 0.94 0.11

a: 62.9

b: 0.86 0.1

Excluded data

Data of the 1st

monitoring period

Data of the 2nd

monitoring period

10 10 10 10 10 10 10

10 10 10 10 10 10 10 10

Volume of events [m3]

(c)

(d)

Cumul. volume distribution

Fitted power law

Cumul. volume distribution

Fitted power law

Normalized annual frequency [number year ]

a: 256.5

b: 0.80 0.07

a: 153.1

b: 0.81 0.06

Excluded data

Data of the 3rd monitoring period

Data of the overall

survey period

10 10 10 10 10 10

Volume of events [m3]

Figure 10. Cumulative volume distribution of the rockfall observed during the rst (a), the second (b), and the third monitoring period (c) and over the entire study time of 16 months (d). For each dataset, the power law is tted for volumes larger than 0.1 m3. Below this threshold volume, the distribution exhibits a rollover that progressively reaches an almost constant frequency for the smallest detected volumes.

net gain of storage in the headwater was therefore inferred.

In October, a succession of low-intensity rainfall events triggered sediment transport in the torrent that accumulated in the sediment trap with a volume of at least 302 m3 36. The

sediment budget indicates clearly a recharge of 229 m3 31,

a transfer of debris that was stored mostly in the distal part of the torrent. Throughout the winter, a gradual incision was ob-served all along the torrent, resulting from frequent periods of low-intensity rainfall as well as snowmelt. Due to maintenance (dredging), the sediment trap was disturbed and no reliable data were available. In any case, no sign of significant sediment activity was detected. A new debris ow on6 June deposited 3320 m3 176 in the sediment trap. This

time, a certain supply of sediment from the headwater was observed ( 270 m3). This event was followed by a series of

intense rainfall events without much reworking in the distal part, suggesting that any signicant transfer occurred into the torrent downstream. The in-torrent storage changes and estimated recharge budgets are shown for each monitoring period in Fig. 11.

5 Synthesis

The overall transfer dynamics, from debris source zone to the apex of the fan, are illustrated in Fig. 12. The volumes detected during the 16-month study period reveal a net export of 3378 m3 361 of sediment from the headwa

ter to the main torrent (Table 5). The overall rock slope yield is 3575 m3 30 for a volume of erosion reaching

3129 m3 150 on the hillside and 1809 m3 92 in the chan

nel complex. The volume of deposition, induced by both debris production and material reworking, yields a total volume of 5135 m3 251, of which only 1382 m3 56 (27 %)

is linked to the channel complex. In the main torrent, the sediment transfer was relatively large ( 20 000 m3; net storage

change 4950 m3 118) and essentially related to the oc

currence of two debris ows (Theule et al., 2012), depleting signicantly the in-torrent sediment storage of the distal parts (entrainment zone). Material deposited in the sediment trap for the survey period yields 6075 m3 45. During the

www.earth-surf-dynam.net/4/489/2016/ Earth Surf. Dynam., 4, 489513, 2016

502 A. Loye et al.: Headwater sediment dynamics

In-channel storage change per unit length Blue: eposition Red: rosion Black: alance

Sedim ent transport Cumulative olume

In-channel storage change per unit length Blue: deposition Red: erosion Black: balance

Sedim ent ransport Cumulative volume

Sedim ent deposition

Sedim ent trap

7000

5000

Headwater outlet

Sediment trap

Entrainment

zone

Transport zone

Deposition

zone

5000

Unit volume change (m m )

Sediment transport (m )

Volume Change (m3)

20 (06/07/2009 - 28/08/2009)

20 (30/08/2009 - 07/10/2009)

20 (08/10/2009 - 12/11/2009)

20 (13/11/2009 - 01/06/2010)

20 (02/06/2010 - 08/06/2010)

20 (06/07/2009 - 28/08/2009) (debris-flow)

20 (30/08/2009 - 07/10/2009)

20 (08/10/2009 - 12/11/2009)

20 (13/11/2009 - 01/06/2010)

20 (02/06/2010 - 08/06/2010) (debris-flow)

736-842 m 3

(debris flow)

3000

4000

1000

1st

2nd

3rd

3000

-1000

-5

2000

-3000

-10

1000

1873 (62)m 3

-15

-5000

-20

5000

-7000

Hillside

4000

3000

-5

2000

736-842 m 3

-10

7000

5000

3000

Volume Change (m3)

1000

-1000

-3000

-5000

-7000

Hillsid

1000

0 m 3

-15

-20

5000

4000

3000

-5

2000

198-260 m 3

-10

1000

266-338 m 3

535-625 m 3

266-338 m 3

535-625 m 3

-15

-20

5000

4000

3000

-5

2000

7000

-10

1000

-15

5000

Volmue Change (m3)

-20

5000

3000

Downstream Distance from Alluvial Storage Apex (m)

(debris flow)

4000

1000

3000

-1000

-5

3320 (176)m3

2000

-3000

-10

1000

0-537 m 3

0 537 m 3

3320 (176)m

-5000

-15

-20

-7000

0 500 1000 1500

Downstream Distance from Alluvial Storage Apex (m)

0 500 1000 1500

Hillside

Downstream

Downstream distance from production zone Downstream distance from production zone

Figure 11. Torrent in-channel storage changes per unit length and sediment budgets of cumulative volumes transported in the torrent from the headwater outlet to the sediment trap downstream for each monitoring period (MP). The torrent recharge (sediment input) was estimated given the in-storage change and the volume deposited in the sediment trap (see Table 4 for details on values) (modied from Theule et al., 2012).

autumn, bedload transport of hundreds of square metres contributed to sediment recharge throughout the torrent.

In the springmidsummer period, the hillside sediment budget yields a total rock slope production of 99 m3 6,

for a volume of erosion of 547 m3 50 and deposi

tion of +408 m3 35 (Table 5). This suggests that about

238 m3 61 of material was supplied the channel complex,

originating almost exclusively from material re-entrainment in gullies (Fig. 13). The sediment budget of the channels indicates a signicant reduction in storage (487 m3 44), com

prising large and continuous incisions (636 m3 43) in

Fig. 6ods showing

the upper reaches and material aggradation (+149 m3 11)

in the lower reaches resulting mostly from zones of transient redeposition. This results in a recharge of the torrent of +726 m3 103 for this survey period.

During the late summerautumn season, the total volume of hillside erosion is 640 m3 27, due to a widespread

scouring of the tributary gullies located east and south-east of the headwater (Fig. 14). The total volume of rock slope production (50 m3 3) and deposition (+182 m3 12) re

mained low. Overall, the sediment budget indicates that the hillslope contributed about 510 m3 30 of sediment to the

Earth Surf. Dynam., 4, 489513, 2016 www.earth-surf-dynam.net/4/489/2016/

A. Loye et al.: Headwater sediment dynamics 503

Rock slope

Hillside

12 order channels

Torrent trunk

Sediment trap

(a)

April 2009July 2010

(b)

7000

AprilAugust 2009

3575 m3

5000

Net balance

Net erosion

Net deposition

3 3000

Volume change (m )

1000

3129 m3

E D

-1000

3753 m3

-3000

-5000

Torrent

Headw ater

SeptemberNovember 2009

-7000

Hillside 12 Order

channel

E ntrainment

zone

Transport

zone

D eposition

zone

1809 m3

7000

1382 m3

- 4950 m3

5000

Net balance

Net erosion

Net deposition

3000

Volume change (m )

1000

+ 3378 m3

-1000

-3000

-5000

Headwater Torrent

November 2009July 2010

-7000

Hillside 12 Order

channel

E ntrainment

zone

Transport

zone

D eposition

zone

7000

Net balance

Net erosion

Net deposition

5000

3 3000

+ 6075 m3

Volmue change (m )

1000

-1000

-3000

-5000

Headw ater

Torrent

Rockfall Erosion

Deposition

-7000

Hillside 12 O rder

channel

Entrainm ent

zone

Transport

zone

D eposition

zone

Storage change

Net headwater sed. input

Net torrent sed. output

Figure 12. Overall sediment budget (a) and net sediment balance (b) for each monitoring period showing the overall transfer dynamics from debris source zone in the headwater to the apex of the fan through the torrent observed during the period of investigation.

channel reaches (Table 5). The sediment budget of the channels yields 522 m3 20 of erosion for +127 m3 13 of de

position. This is characterized by bedload reworking in both low-order and trunk channels and a progressive transfer of

+904 m3 51 of material into the torrent.

During winterspring 2010, a total deposition volume of

+3163 m3 147 is recorded on the hillside for an eroded

volume of 3129 m3 150. A relatively large produc

tion of debris (3424 m3 89) is observed (Table 5). The

net sediment balance on the hillside yields a supply of

+2203 m3 187 of sediment into the channels, and the

net sediment balance for the channel complex indicates an increase in in-channel sediment storage of +455 m3 47

for a total volume of deposition of 1105 m3 36 and ero

sion of 651 m3 29 due to large bed scouring zones in the

downstream reaches. Sediment transfer into the torrent is 1749 m3 199 (Fig. 15).

6 Discussion

6.1 Debris supply through rock slope production

Debris production from rock walls shows a strong seasonal pattern. The great majority of recorded rock instabilities in both magnitude (95 %) and frequency (75 %) occurred during the cold period. Previous studies of the calcareous cliffs near Grenoble, which have a similar morphotectonic context, revealed that freezethaw cycles are the main triggering factor of rockfall (Frayssines and Hantz, 2006). Ice jacking can cause microcrack propagation, leading to failure (Matsuoka and Sakai, 1999). Along the eastern ridge, the bedrock surface is often highly fractured, suggesting frost shattering. The spatial pattern of rockfall also strongly suggests a tectoniclithological inuence that can be explained by differential erosion between the successive limestone and marl beds. In the rock wall series on the west side, the monoclinal conguration of the bedding, combined with a strong difference of

www.earth-surf-dynam.net/4/489/2016/ Earth Surf. Dynam., 4, 489513, 2016

Fig. 6 ods

504 A. Loye et al.: Headwater sediment dynamics

Table 5. Overall headwater sediment budget recorded during the three survey periods and net sediment balance of the 16 months of monitoring. Sediment budgets for each catchment subsystem are detailed in the Supplement.

First monitoring Volume total (m3)period Hillside Channel Headwater

Rockfall 99.4 5.9 99.4 5.9

Deposition 408.2 35.4 149.2 10.9 557.4 46.3

Erosion 547.2 49.5 636.4 43.3 1183.5 92.8 Subtotal 238.3 61.2 487.2 44.7 725.6 103.9

Second monitoring Volume total (m3)period Hillside Channel Headwater

Rockfall 50.5 3.0 50.5 3.0

Deposition 181.8 12.2 127.2 8.0 309.0 20.5

Erosion 639.8 27.1 522.5 19.4 1162.3 46.4 Subtotal 508.5 29.9 395.3 23.4 903.7 50.9

Third monitoring Volume total (m3)period Hillside Channel Headwater

Rockfall 3424.9 89.1 3424.9 21.4

Deposition 3163.5 147.9 1105.5 36.4 4269.0 175.6

Erosion 1941.6 72.8 650.8 28.8 2592.4 91.6 Subtotal 2203.0 187.4 454.7 46.5 1748.3 199.2

Total Volume total (m3)monitoring Hillside Channel Headwater

Rockfall 3574.7 97.9 3574.7 30.3

Deposition 3753.5 195.6 1381.9 55.6 5135.4 251.3

Erosion 3128.5 149.4 1809.7 91.3 4938.2 240.8 Subtotal 2949.8 264.9 427.8 106.9 3377.6 361.4

competency between stratigraphic sequences, gives rise to an overhanging formation highly susceptible to failure. On the east side, the bedding is mostly cataclinal and approaches dip slope, depending on the slope. Rock failures initiated by planar sliding on bedding planes were observed.

The observed debris production follows a power law distribution in a range covering at least 3 orders of magnitude [100103]. The exponent b is slightly higher than the average value reported for the Grenoble cliffs ([0.40.7]; Hantz, 2011) but is in agreement with other short inventories covering a lower range of volume ([102102]; Hungr et al., 1999;

Dussauge et al., 2003). Inventories dominated by small volumes tend to increase the b value, compared to the ones covering rather large volumes (Stark and Hovius, 2001). Above 100 m3, the deviation from the power law may be attributed to the short period of sampling for events of such a large magnitude. The rollover encountered towards small volumes results most likely in the under-detection of the number of events. This sampling bias is far above the minimum volume of detection (0.006 m3); therefore, another behaviour characterizing the failure of small volumes cannot be excluded.

This may take the form of a physical erosion process that differs from the one inuencing larger instabilities, which are controlled primarily by the geometrical and geomechanical properties of the rock mass (Selby, 1993; Sauchyn et al., 1998), and tectonic weakening (Cruden, 2003; Coe and Harp, 2007). As observed here, low-magnitude rockfall events represent a low proportion of overall debris supply, even though they vary locally from 1 or 2 orders of magnitude in volume over time. The total amount of sediment available is only signicantly inuenced by high-magnitude instabilities (Fig. 16).

Previous sediment budgets derived from topographic measurement using stereophotogrammetry estimated the highest erosion rates over an average of 40 years to range from 10.8 to 17.8 mm yr1 in the headwater (Veyrat-Charvillon and

Memier, 2006). Given the large uncertainty of the approach, and the fact that they measured the hillslope and thalweg geomorphic activity, these values are broadly consistent with the erosion rate derived here from a short-period rockfall inventory by assuming the possible occurrence of rockslide magnitudes [106107]. Considering that the power law is valid

Earth Surf. Dynam., 4, 489513, 2016 www.earth-surf-dynam.net/4/489/2016/

A. Loye et al.: Headwater sediment dynamics 505

Figure 13. Overall headwater sediment budget observed during the rst monitoring period revealing the sediment dynamics through the springsummer season and the net balance of sediment recharge in the downstream torrent for several months preceding the August 2009 debris ow.

for larger slope failures, a 7500 m3 event can be expected every 10 years and a 120 000 m3 event every 100 years. The average debris production ranges between 5587 241 and

12 903 305 m3 yr1, assuming a maximum potential ero

sion of 105 and 107 m3 respectively over several centuries (Table 6). No historical Manival rockslide exists to support this estimation. The large old rock deposit ( 6.1 Mm3) of

the upper catchment is the largest detected event, but it may have formed from several rock collapses. The rockfall inventory of the Grenoble cliffs reports volumes smaller than 105 m3 for the last century and 107 m3 since the 17th century (Hantz et al., 2003). Such a magnitude is also likely at the Manival. A mean rate of rock slope erosion of approximatively 10 mm yr1. 10 000 m3 yr1 can be therefore expected in the upper catchment over the century.

Upstream from the Manival channel, the scouring of debris slopes and scree hollows triggered by rock slope production accounted for about 40 % of the net erosion recorded during the autumn period and 25 % in the Baure Ravine over the entire study period. The spatial pattern of geomorphic work showed that hillslope process activity was observed principally in gullies and scree slopes situated directly below active rock walls. The dominant mode of debris supply in the Manival headwater is therefore highly episodic, implying a great spatial heterogeneity in sediment recharge rates.

6.2 Debris supply through hillslope activity

As rock slope activity was very limited from spring to autumn, hillslope geomorphic activity dominated sediment

www.earth-surf-dynam.net/4/489/2016/ Earth Surf. Dynam., 4, 489513, 2016

506 A. Loye et al.: Headwater sediment dynamics

Figure 14. Overall headwater sediment budget observed during the second monitoring period revealing the sediment dynamics and the net balance of sediment recharge in the downstream torrent during the autumn.

recharge during this period. Until the end of August, hillside gullies and low-order channels remain almost inactive in terms of sediment delivery. Conversely, the autumn period was characterized by a general increase in the intensity of geomorphic activity. Continuous scouring and the relative paucity of deposition features from hillside gullies as well as clear incisions and micro debris ows in channel reaches indicate that mobilized material was almost entirely entrained downstream by runoff. For the entire area, the hillside contribution represents on average a volume 5 times larger than the volume that was observed in spring and summer, and channel bed reworking was of a much larger magnitude as well.

During winterspring 2010, the total volume of deposition recorded on the hillside signicantly exceeds the rate of deposition recorded so far, resulting from the huge increase in

debris production that can be attributed to the winter according to observations carried out in the preceding spring. Hill-slope and gully erosion remain on average comparable to the volumetric transfer of sediment observed in the preceding autumn, implying a clear connectivity.

These negative sediment balances in all sediment cascade components suggest a very high degree of connectivity between hillside and channels in autumn, and hillside fan deposits observed in early spring along low-order channel banks reect an effective hillslopechannel coupling. This differs from effective sediment transfer occurring mostly during the summer (e.g. Berger et al., 2011; Cavalli et al., 2013).

Earth Surf. Dynam., 4, 489513, 2016 www.earth-surf-dynam.net/4/489/2016/

A. Loye et al.: Headwater sediment dynamics 507

Figure 15. Overall headwater sediment budget observed during the third monitoring period revealing the sediment dynamics through the winterspring and the net balance of sediment recharge in the downstream torrent for the period preceding the June 2010 debris ow.

6.3 Sediment recharge of the torrent

The sediment input, back-calculated from the in-torrent storage changes, is consistent with the net sediment output recorded from the headwater for the rst two survey periods. In the torrent, the morphological monitoring that started in July revealed almost no sediment recharge (< 70 m3) and is coherent with observations made in the summer in the upper catchment. The headwater sediment output must have accumulated before, probably mobilized as bedload by common runoff events in spring. In autumn, both budgets are approximately equal (1018 84 m3 against 904 m3 51), consid

ering that few segments between both entities are missing and that both budgets were in volumetric units, despite having different sediment densities. The morphological budget

indicates that the torrent experienced a net recharge in the distal part and emphasizes the clear connectivity from the production zones to the torrent, as mentioned before. In the third survey period, the headwater sediment balance indicates a net export of debris (1749 m3 199), whereas the

morphological monitoring detected no signicant volumes of debris entering the main torrent. Even the recharge (sediment input, Fig. 11) measured during the June debris ow events (< 600 m3) remains far below the transfer of sediment recorded upstream in the headwater. This discrepancy may result from material deposition occurring in the non-monitored segments at the headwater outlet. But eld studies did not conrm this. The analysis of past series of sediment budgets performed in the upper Manival catchment

www.earth-surf-dynam.net/4/489/2016/ Earth Surf. Dynam., 4, 489513, 2016

508 A. Loye et al.: Headwater sediment dynamics

Volume maximum

V1 = 3600 m 3

V2 = 5000 m 3

V3 = 10 000 m3

V = 25 000 m

4 3

V5 = 50 000 m3

100

V1 V2

V1 V2 V3

Erosion rate [mm year ]

Contribution per volume [%]

-1

0 0 1000 5000 9000 10 000

0 0

2000 4000 6000 8000

3000 7000

Volume of event [m3]

1.6

Volume maximum

V6 = 100 000 m3

V = 500 000 m

7 3

V8 = 1000 000 m 3

V = 5 000 000 m

9 3

V6 V7 V8

1.4

1.2

Erosion rate [cm year ]

Contribution per volume [%]

-1

0.8

0.6

0.4

0.2

0 0 1000

200 400 600 800

100 300 500 700 900

100

Volume of event [1000 m

Figure 16. Continuous lines: erosion rate as function of size of events for a certain volume of production (potential maximum volume V1...9), considering that rockfall volume distribution observed at Manival follows power law behaviour (Table 6). Dashed lines: contribution of each class of volumes to the erosion rate showing the signicant effect of large slope failures. For a maximum eroded volume of 3600 m3 yr1 (V1), the 1000 m3 rockfall event contributes 60 %, while events less than 100 m3 induce less than 20 % of erosion, although they are of a much higher frequency; a 100 000 m3 rockslide would generate 70 % of total eroded material of 500 000 m3 (V7) over a century.

(Veyrat-Charvillon, 2005) reveals that the springearly summer time currently exhibits a period of recharge following a phase of discharge within a short time lapse depending on the hydrometeorological and snowmelt conditions. The most reasonable explanation is therefore the relatively long time interval between measurements, such as the successive reworking of bedload transport suppressing the cut and ll pattern and masking the short-term behaviour of sediment transfer in the torrent. This is a well-known issue when working with channelized hillslope processes (Fuller and Marden, 2010). Although this monitoring aspect concerns the topo-graphic changes recorded by TLS in the headwater as well, geomorphic activity, such as micro debris ows and contin-

uous channel bed degradation, strongly suggests phases of sediment recharge preceding the debris ow events, which would be consistent with other studies (e.g. Brayshaw and Hassan, 2009; Marchi et al., 2002, Bennett et al., 2012).

6.4 Possible causes of seasonal uctuations in debris supply

The Manival headwater experienced low geomorphic activity through the summer, and consequently low sediment recharge of the torrent, even though rainstorms were of sufciently high intensity to trigger debris ows of signicant magnitude in torrent. Considerations of the temporal pattern

Earth Surf. Dynam., 4, 489513, 2016 www.earth-surf-dynam.net/4/489/2016/

A. Loye et al.: Headwater sediment dynamics 509

Table 6. Rock slope debris production rate estimated from the inventory analysis using power law distribution of volume for potential rockfall (Fig. 10).

Class of 103102 102101 1011 110 101102 102103 103104 104105 105106 106107 volume (m3)

Measured 143 (112.5) 742 (583.7) 789 (620.7) 168 (132.2) 19 (14.95) 3 (2.36) 1 (0.79) frequency (per year)

Calculated 36 990 5621 854 130 19.7 3.0 0.46 0.069 0.011 0.0016

frequency 4366 581 86 9.6 1.2 0.14 0.015 0.0013 1 10

4 1.2 10

Cumulative 1467 1355 772 152 19 3.1 0.79 measured frequency

Cumulative 43 619 6629 1007 153 23 3.5 0.54 0.08 0.01 0.0016

calculated 5043 677 97 11 1.58 0.198 0.018 0.0014 1.1 10

4 1.2 10

frequency

Fallen volume 102 155 236 358 544 827 1257 1911 2904 4413

per year (m3) 12 16 19 26 32 37 39 32 8 51

Total fallen 298 454 689 1047 1592 2419 3676 5587 8491 12 903

volume per 43 59 79 105 136 172 210 241 249 305 year (m3)

Cliff area 826 804 m2 (only the topographic rock slope surface)

Erosion rate 0.36 0.54 0.83 1.3 1.9 2.9 4 6.8 10.2 15.6

(mm) 0.05 0.07 0.1 0.1 0.2 0.2 0.3 0.3 0.3 0.4

of sediment transfer and the analysis of erosion features, like alternating areas of scouring and inlling in gullies, suggest that runoff still has an important role in the headwater sediment dynamics. A clear relation between sediment transfer magnitude and precipitation remains complex, however (Fig. 3), as is often the case in mountainous catchments (Van Steijn, 1996; Bovis and Jakob, 1999; Pelni and Santilli, 2008). The enhanced geomorphic activity observed in the hillside of several headwater subsystems, for instance during the autumn period, induced a simultaneous yet highly heterogeneous response in their channel reaches. A signi-cant increase in bed incision and reworking similar to debris ow was observed in the upper reaches of the Manival subcatchment, implying an important sediment transfer. In contrast, the activity of other channel reaches was reduced by half, e.g. in Roche Ravine, or even remained geomorphically much less active, with only little sediment recharge.

Considering that meteorological conditions were similar, this opposite behaviour may only be explained by a certain depletion of debris availability. This reduction in sediment yield can come not only within a supply-limited regime of the contributing area (Jakob et al., 2005; Glade, 2005) but also from the fact that check dams, like bedrock-dominated reaches, inhibit channel bed incision. Hence, the sediment storage has to be relled either from the contributing hillside or from the upstream mass movement. A similar observation can be drawn from the Grosse Pierre Ravine sediment budget, whose gully downslope remained completely disconnected from the head of the subcatchment over the entire study period. Although this ravine is very steep and incises

the large old rock deposits, no geomorphic work was ob-served, resulting most likely from the absence of debris supply from upstream. Hillside sediment delivery seems therefore to be clearly a limiting factor to sediment yield from low- to high-order channels and thus to the sediment recharge rate of the debris ow torrent downstream. As the occurrence of bedload transport and micro debris ows is controlled predominantly by the availability of sediment, even very intense rainstorm-derived runoff does not automatically lead to a signicant transfer of sediment from the hillside to low-order channels in the case of material depletion.

Nevertheless, this behaviour is somehow equivocal, considering the fact that the transport capacity of ephemeral stream runoff and sheetwash related to high-intensity rainstorms is larger than the one generated by low-intensity long-duration rainfall, above all, when gully material (like in Manival) can be characterized as coarse and poorly sorted rockfall-fragment-derived debris. Lenzi et al. (2003) interpreted the annual uctuation in sediment yield as the effect of sediment source destabilization or reactivation following a high-magnitude ow event, which facilitates material entrainment by subsequent runoff. Johnson and Warburton (2006) refer to the inuence of sediment source characteristics in the control of hillslope sediment discharge. The explanation may be that the 25 August rainstorm dramatically altered the debris sources in a way that the autumn rainfalls which, although they were of lower intensity, had a longer ood time were able to transfer sediment downs-lope. Excess pore-uid pressure in debris deposits can persist for days to weeks after sediment emplacement (Major

www.earth-surf-dynam.net/4/489/2016/ Earth Surf. Dynam., 4, 489513, 2016

510 A. Loye et al.: Headwater sediment dynamics

and Iverson, 1999; Major, 2000), making debris deposits geotechnically less stable.

Although they depend on the local geomorphological setting, such as slope gradient, local topographic hollow, and degree of convergence (Reneau et al., 1990; Stock and Dietrich, 2006; Mao et al., 2009), these observations tend to show that long-lasting rainfall reduces the stability of the coarse surface layer that armours the gullies and scree slopes. This in turn affects the amount of debris supply from the hillside, despite the ow capacity and sediment availability.

7 Conclusions

This investigation of a yearly pattern of sediment dynamics underlines the fact that the seasonal cycle of sediment discharge from the headwater supplying the Manival torrent with debris consisted of two phases of recharge: one phase in early spring, linked to enhanced debris production and runoff conditions, and a second phase in autumn, during long periods of rainfall. Furthermore, the occurrence of the debris ow events was conditional on a net sediment delivery toward the torrent.

Overall, the torrent effectiveness seems to be controlled early in the year, from winter to spring, by sediment production and later in the year by the ability of hydrological effects to weaken the remnant debris sources, with debris availability being only one of the limiting factors at the Manival torrent. The rate of sediment delivery, directly recharging both hillside and low-order channels, is controlled by high-magnitude slope failure of moderate frequency which occurred mostly during winter time. Consequently, material re-entrainment concentrates locally in specic tributary gullies. The delivery of sediment to the torrent may be related to the hydrometeorological conditions since the last rainstorm rather than to ow capacity directly. Low-order reaches contribute signicantly to the sediment delivery mechanism of the catchment headwater by controlling storage and routing processes. Hence, the recharge threshold required for a new debris ow to occur at the Manival depends primarily on the short-term debris supply, partly derived from the rate of rock slope sediment production and partly derived from mobilizing debris on the hillside. The rate of sediment recharge in the torrent is, however, greatly intermittent, since production and entrainment are both highly stochastic processes. This regime of headwater sediment delivery may have been identied in other nearby mountain environments, but very little literature exists (Alvarez and Garcia Ruiz, 2000; Veyrat-Charvillon, 2005; Berger et al., 2011) that has explored the timescale of sediment discharge in sufcient detail, e.g. on a seasonal basis.

Debris ow magnitudes have so far been mostly determined based on volume estimates derived from past events, reducing the susceptibility analysis to the known history.Monitoring of the in-storage changes within the torrent linked to the debris supply can help to improve knowledge

on the recharge threshold leading to debris ow activity and therefore on their prediction. According to the rock slope production observed in this study, 10 000 m3 yr1 of debris supplying the headwater channels can be expected in Manival over a century. Despite the multiplicity of sediment sources and the mode of transfer operating on different spatial and temporal scales, the pattern of processes governing the sediment dynamics can be considered precisely on a seasonal basis using TLS techniques. Therefore, maximum sediment discharges from the torrent system can be specied. Without direct measurement of the rate of sediment ux and of the coupling between hillslope and channel processes, this cannot be rigorously determined. The timing of sediment budget monitoring is, however, a crucial aspect for their later interpretation.

The Supplement related to this article is available online at http://dx.doi.org/10.5194/esurf-4-489-2016-supplement

Web End =doi:10.5194/esurf-4-489-2016-supplement .

Acknowledgements. The authors would like to thank their colleagues at IGAR and IRSTEA Grenoble (ex. CEMAGREF), in particular A. Pedrazzini and M.-H. Derron, for their valuable comments during the preparation of this publication. This study was entirely supported by the University of Lausanne, except for the event-based cross-section surveys that were funded by the Ple Grenoblois dtude et de recherche pour la prvention des risques naturels. The ONF-RTM38 is acknowledged for making the access to the upper Manival catchment easier. This publication beneted from an interactive discussion with O. Sass and two other anonymous reviewers and from proofreading by S. Conway.

Edited by: S. Conway

References

Abelln, A., Jaboyedoff, M., Oppikofer, T., and Vilaplana, J. M.:

Detection of millimetric deformation using a terrestrial laser scanner: experiment and application to a rockfall event, Nat.Hazards Earth Syst. Sci., 9, 365372, doi:http://dx.doi.org/10.5194/nhess-9-365-2009

Web End =10.5194/nhess-9-365- http://dx.doi.org/10.5194/nhess-9-365-2009

Web End =2009 , 2009.

Aki, K.: Maximum likelihood estimate of b in the formula logN = a

bM and its condence limits, B. Earthq. Res. I. Tokyo, 43, 237 239, 1965.

Alvarez, B. and Garcia-Ruiz, J.: Variability of sediment yield from a high mountain catchment, central Spanish Pyrenees, Arct.Antarct. Alp. Res., 32, 478484, 2000.

Anderson, E. S., Thompson, J. A., and Austin, R. E.: LiDAR density and linear interpolator effects on elevation estimates, Int. J.Remote Sens., 26, 38893900, 2005.

Ashmore, P. E. and Church, M. A.: Sediment transport and river morphology: A paradigm for study, in: Gravel-Bed Rivers in the Environment, edited by: Klingeman, P. C., Beschta, R. L., Komar, P. D., and Bradley, J. D., Water Resource Publications, LLC, Highlands Ranch, CO, USA, 115148, 1998.

Earth Surf. Dynam., 4, 489513, 2016 www.earth-surf-dynam.net/4/489/2016/

A. Loye et al.: Headwater sediment dynamics 511

Benda, L. and Dunne, T.: Stochastic forcing of sediment supply to channel networks from landsliding and debris ow, Water Re-sour. Res., 33, 28492863, 1997.

Bennett, G. L., Molnar, P., Eisenbeiss, H., and McArdell, B. W.: Erosional power in the Swiss Alps: characterizing slope failure at the head of the Illgraben, Earth Surf. Proc. Land. 37, 1627 1640, doi:http://dx.doi.org/10.1002/esp.3263

Web End =10.1002/esp.3263 http://dx.doi.org/10.1002/esp.3263

Web End = , 2012.

Berger, C., McArdell, B. and Schlunegger, F.: Sediment transfer patterns at the Illgraben catchment, Switzerland: Implication for the time scales of debris ow activities, Geomorphology, 125, 421432, 2011.

Berti, M., Genevois, R., LaHusen, R., Simoni, A., and Tecca,P. R.: Debris ow monitoring in the acquabona watershed on the Dolomites (Italian alps), Phys. Chem. Earth. Pt. B, 25, 707715, 2000.

Besl, P. J. and McKay, N. D.: A method for registration of 3-D shapes, IEEE T. Pattern Anal., 14, 239256, 1992.

Bitelli, G., Dubbini, M. and Zanutta, A.: Terrestrial laser scanning and digital photogrammetry techniques to monitor landslide bodies, in: Proceedings of the XXth ISPRS Congress Geo-Imagery Bridging Continents, XXXV, part B5, Istanbul, Turkey, 1223 July 2004, ISPRS, 246251, 2004.

Bovis, M. J. and Jakob, M.: The role of debris supply conditions in predicting debris ow activity, Earth Surf. Proc. Land., 24, 1039 1054, 1999.

Brayshaw, D. and Hassan, M. A.: Debris ow initiation and sediment recharge in gullies, Geomorphology, 109, 122131, 2009.Bremer, M. and Sass, O.: Combining airborne and terrestrial laser scanning for quantifying erosion and deposition by a debris ow event, Geomorphology, 138, 4960, 2011.

Brochot, S., Coeur, D., Lang, M. and Naulet, R.: Historique Isre et torrents afuents. Utilisation de linformation historique pour une meilleur dnition du risque dinondation (rapport), Cemagref/Achtys, LyonGrenoble, 248 pp., 2000.

Brodu, N. and Lague, D.: 3-D terrestrial lidar data classication of complex natural scenes using a multi-scale dimensionality criterion: Applications in geomorphology, ISPRS J. Photogramm. 68, 121134, 2012.

Buckley, S., Howell, J., Enge, H., and Kurz T. : Terrestrial laser scanning in geology: data acquisition processing and accuracy considerations, J. Geol. Soc. London, 165, 625638, 2008.Burrough, P. and McDonnell, R.: Principals of Geographic Information Systems, Oxford University Press, Oxford, UK, 333 pp., 1998.

Cannon, S. H., Gartner, J. E., Parret, C., and Parise M.: Wildre-related debris ow generation through episodic progressive sediment-bulking process, western USA. In: Debris-ow hazard mitigation: Mechanics, Prediction, and Assessment,edited by: Rickenmann, D. and Chen, L., Millpress, Rotterdam, the Netherlands, 7182, 2003.

Cavalli, M., Trevisani, S., Comiti, F., and Marchi, L.: Geomorphometric assessment of spatial sediment connectivity in small Alpine catchments, Geomorphology, 188, 31-41, 2013.Chandler, J. H. and Brunsden, D.: Steady state behaviour of the

Black Ven mudslide : the application of archival analytical photogrammetry to studies of landform change, Earth Surf. Proc.Land., 20, 255275, 1995.

Charollais, J., Dondey, D., Ginet, C., Lombard, A., Muller, J. P., Rosset, J., and Ruchat, C.: Carte gol. France (1/50.000 ), Feuille Domne (33-34), Orlans, B.R.G.M, 1986.

Coe, J. A. and Harp, E. L.: Inuence of tectonic folding on rockfall susceptibility, American Fork Canyon, Utah, USA, Nat. Hazards Earth Syst. Sci., 7, 114, doi:http://dx.doi.org/10.5194/nhess-7-1-2007

Web End =10.5194/nhess-7-1-2007 http://dx.doi.org/10.5194/nhess-7-1-2007

Web End = , 2007.

Coe, J. A., Whitney, J. W., and Harrington, C. D.: Photogrammetric analysis of Quaternary hillslope erosion at Yucca Mountain, Nevada, Geol. Soc. Am. Astr. Progr., 25, 1993.

Cruden, D. M.: The shapes of cold, high mountains in sedimentary rocks, Geomorphology, 55, 249261, 2003.

Decaulne, A. and Saemundsson, P.: Spatial and temporal diversity for debris-ow meterotological control in subarctic oceanic periglacial environments in Iceland, Earth Surf. Proc. Land., 32, 19711983, 2007.

Dewez, T. and Rohmer, J.: Probabilistic rockfall hazard: empirical computation based on ground-based lidar observations in Mesnil-Val, Normandy. Journe Ala Gravitaires, 78 Septembre 2011, Strasbourg, France, 104115, 2011.

Dussauge, C., Grasso, J.-R., and Helmstetter, A.: Statistical Analysis of Rock Fall Volume Distributions: Implications for Rock Fall Dynamics, J. Geophys. Res.-Sol. Ea., 108, 211, 2003.

Dussauge-Peisser, C., Helmstetter, A., Grasso, J.-R., Hantz, D., Desvarreux, P., Jeannin, M., and Giraud, A.: Probabilistic approach to rock fall hazard assessment: potential of historical data analysis, Nat. Hazards Earth Syst. Sci., 2, 1526, doi:http://dx.doi.org/10.5194/nhess-2-15-2002

Web End =10.5194/nhess-2-15-2002 http://dx.doi.org/10.5194/nhess-2-15-2002

Web End = , 2002.

El-Sheimy, N., Valeo, C., and Habib, A.: Digital terrain modeling: acquisition, manipulation, and applications, Artech House, Boston, MA, USA, 257 pp., 2005.

Frayssines, M. and Hantz, D.: Failure mechanisms and triggering factors in calcareous cliffs of the Subalpine Ranges (French Alps). Eng. Geol. 86: 256270, 2006.

Fuller, I. C. and Marden, M.: Rapid channel response to variability in sediment supply: Cutting and lling of the Tarndale Fan, Waipaoa catchment, New Zealand, Mar. Geology, 270, 4554, 2010.

Gardner, J. S.: Rockfall: a geomorphic process in high mountain terrain, The Albertan Geographer, 6, 1520, 1970.

Gidon, M.: Gologie de la Chartreuse Sentiers de la Chartreuse:

Circuit de la Dent de Crolles, Association A la dcouverte du Patrimoine de Chartreuse, publ. 1d, 1 edn., 20 pp., Fig. 9, available at: http://www.Geol-alp.com

Web End =www.Geol-alp.com (last access: 18 April 2014), 1991. Glade, T.: Linking debris-ow hazard assessments with geomorphology, Geomorphology, 66, 189-213, 2005.

Gruffaz, F.: Torrent du Manival (Isre), Etude de basin et de la plage de dpts torrentiels (rapport), RTM/ONF, Grenoble, France, 66 pp., 1997.

Guzzetti, F., Peruccacci, S., Rossi, M., and Stark, C. P.: The rainfall intensityduration control of shallow landslides and debris ows: an update, Landslides, 5, 317, 2008.

Hantz, D.: Quantitative assessment of diffuse rock fall hazard along a cliff foot, Nat. Hazards Earth Syst. Sci., 11, 13031309, doi:http://dx.doi.org/10.5194/nhess-11-1303-2011

Web End =10.5194/nhess-11-1303-2011 http://dx.doi.org/10.5194/nhess-11-1303-2011

Web End = , 2011.

Hantz, D., Dussauge-Peisser, C., Jeannin M., and Vengeon, J.-M.: Rock fall Hazard: from expert opinion to quantitative evaluation. Symposium Geomorphology: from expert opinion to modeling (April 2002), Strasbourg, France, 115122, 2002.

www.earth-surf-dynam.net/4/489/2016/ Earth Surf. Dynam., 4, 489513, 2016

512 A. Loye et al.: Headwater sediment dynamics

Hantz, D., Vengeon, J. M., and Dussauge-Peisser, C.: An historical, geomechanical and probabilistic approach to rock-fall hazard assessment, Nat. Hazards Earth Syst. Sci., 3, 693701, doi:http://dx.doi.org/10.5194/nhess-3-693-2003

Web End =10.5194/nhess-3-693-2003 http://dx.doi.org/10.5194/nhess-3-693-2003

Web End = , 2003.

Hooke, J.: Coarse sediment connectivity in river channel systems: a conceptual framework and methodology, Geomorphology, 56, 7994, 2003.

Hungr, O.: Characterizing debris ows for design of hazard mitigation, in: Proceedings of the 5th International Conference on Debris Flow Hazards Mitigation/Mechanics, Prediction, and Assessment, Padua, Italy, edited by: Genevois, R., Hamilton,D. L., and Prestininzi, A., Italian Journal of Engineering Geology and Environment, Book of abstracts, Keynote lecture, 5, doi:http://dx.doi.org/10.4408/IJEGE.2011-03.B-001

Web End =10.4408/IJEGE.2011-03.B-001 http://dx.doi.org/10.4408/IJEGE.2011-03.B-001

Web End = , 2011.

Hungr, O., Evans, S. G., and Hazzard, J.: Magnitude and frequency of rockfalls along the main transportation corridors of southwestern British Columbia, Can. Geotech. J., 36, 224238, 1999.Hungr, O., McDougall, S., and Bovis, M.: Entrainment of material by debris ows. Chapt. 7, in: Debris Flow Hazards and Related Phenomena, edited by: Jakob M. and Hungr O., Springer Verlag, Heidelberg, Germany, in association with Praxis Publishing Ltd, 135158, 2005.

Jaboyedoff, M., Oppikofer, T., Abelln, A., Derron, M. H., Loye,A., Metzger R., and Pedrazzini, A.: Use of lidar in landslide investigations: a review, Nat. Hazards., 61, 528, 2012.

Jakob, M., Bovis, M., and Oden, M.: The signicance of channel recharge rates for estimating debris-ow magnitude and frequency, Earth Surf. Proc. Land., 30, 755766, 2005.

Johnson, R. M. and Warburton, J.: Variability in sediment supply, transfer and deposition in an upland torrent system: Iron Crag, northern England, Earth Surf. Proc. Land., 31, 844861, 2006.Johnson, R. M., Warburton, J., Mills, A. J., and Winter C.: Evaluating the signicance of event and post-event sediment dynamics in a rst order tributary using multiple sediment budgets, Geogr.Ann. A, 92, 189209, 2010.

Lane, S. N., Westaway, R. M., and Murray Hicks, D.: Estimation of erosion and deposition volumes in a large, gravel-bed, braided river using sysnotpic remoe sensing, Earth Surf. Proc. Land., 28, 249271, 2003.

Lenzi, M. A., Mao, L., and Comiti, F.: Interannual variation of suspended sediment load and sediment yield in an alpine catchment, Hydrolog. Sci. J., 48, 899915, doi:http://dx.doi.org/10.1623/hysj.48.6.899.51425

Web End =10.1623/hysj.48.6.899.51425 http://dx.doi.org/10.1623/hysj.48.6.899.51425

Web End = , 2003.

Loye, A.: Budgeting rockfall and modeling sediment delivery in torrent systems, PhD Thesis, University of Lausanne, Switzerland, 448 pp., 2013.

Loye, A., Jaboyedoff, M., Pedrazzini, A., Theule, J., Libault, F., and Metzger, R.: Morphostructural analysis of an alpine debris ows catchment: implication for debris supply, in: Proceedings of the 5th International Conference on Debris Flow Hazards Mitigation/Mechanics, Prediction, and Assessment, Padua, Italy, 711 June 2011, edited by: Genevois, R., Hamilton, D. L., and Prestininzi, A., Italian Journal of Engineering Geology and Environment, 115126, doi:http://dx.doi.org/10.4408/IJEGE.2011-03.B-014

Web End =10.4408/IJEGE.2011-03.B-014 http://dx.doi.org/10.4408/IJEGE.2011-03.B-014

Web End = , 2011.Loye, A., Jaboyedoff, M., Pedrazzini, A., Theule, J., Libault, F., and Metzger, R.: Inuence of bedrock structures on the spatial pattern of erosional landforms in small alpine catchments, Earth Surf. Proc. Land., 37, 14071423, 2012.

Major, J. J.: Gravity-driven consolidation of slurries implications for debris-ow deposition and deposit characteristics, J. Sediment. Res., 70, 6483, doi:1073-130X/00/070-64/03.00, 2000.

Major, J. J. and Iverson, R. M.: Debris-ow deposition effects of pore-uid pressure and friction concentrated at ow margins, Geol. Soc. Am. Bull., 111, 14241434, 1999.

Mao, L., Cavalli, M., Comiti, F., Marchi, L., Lenzi, M. A., and

Aratto, M.: Sediment transfer processes in two Alpine catchments of contrasting morphological settings, J. Hydrol., 364, 88 98, 2009.

Marchi, L., Arattano, M., and Deganutti, A. M.: Ten years of debris-ow monitoring in the Moscardo Torrent (Italian Alps), Geomorphology, 46, 117, 2002.

Matsuoka, N. and Sakai, H.: Rockfall activity from an alpine cliff during thawing period., Geomorphology, 28, 309328, 1999.Oppikofer, T.: Detection, Analysis and monitoring of slope movements by high-resolution digital elevetion models. PhD Thesis, Inst. of Geomatics and Risk Analysis, University of Lausanne, Lausanne, Switzerland, 2009.

Peiry, J. L.: Les torrents de LArve: dynamique des sdiments et impact de lamnagement des bassins versants sur lactivit torrentielle, Rev. Geogr. Alp., 78, 2558, 1990.

Pelni, M. and Santilli, M. Frequency of debris ows and their relation with precipitation: A case study in the Central Alps, Italy, Geomorphology, 101, 721730, doi:http://dx.doi.org/10.1016/j.geomorph.2008.04.002

Web End =10.1016/j.geomorph.2008.04.002 http://dx.doi.org/10.1016/j.geomorph.2008.04.002

Web End = , 2008.

Perroy, R. L., Bookhagen, B., Asner, G. P., and Chadwick, O. A.:

Comparison of gully erosion estimates using airborne and ground-based LiDAR on Santa Cruz Island, California, Geomorphology, 118, 288300, 2010.

Pteuil, C., Maraval, C., Bertrand, C., and Monier, G.: Torrent du

Manival: Schma damnagement et de gestion du basin versant contre les crues, techn. report (unpubl.), Ofce National des Forts, Service de Restauration des terrains en Montagnes de lIsre, Grenoble, France, 107 pp., 2008.

Reneau, S. L., Dietrich, W. E., Donohue, D. J., Jull, A. J. T., and

Rubin, M.: Late Quaternary history of colluvial deposition and erosion in hollows, central California Coast Ranges, Geol. Soc.Am. Bull. 102, 969982, 1990.

Rickenmann, D.: Empirical relationship for Debris Flows, Nat. Hazards, 19, 4777, 1999.

Roering, J. J., Stimely, L. L., Mackey, G. H., and Schmidt, D. A.:

Using DInSAR, airborne LIDAR and archival air photos to quantify landsliding and sediment transport, Geophys. Res. Lett., 36, L19402, 5 pp., doi:http://dx.doi.org/10.1029/2009GL040374

Web End =10.1029/2009GL040374 http://dx.doi.org/10.1029/2009GL040374

Web End = , 2009.

Sauchyn, D. J., Cruden, D. M., and Hu, X. Q.: Structural control of the morphometry of open rock basins, Kananaski region, Canadian Rocky Mountains, Geomorphology, 22, 313324, 1998.Schlunegger, F., Badoux, A., McArdell, B. W., Gwerder, C., Schnydrig, D., Rieke-Zapp, D., and Molnar, P.: Limits of sediment transfer in an alpine debris-ow catchment, Illgraben, Switzerland, Quaternary Sci. Rev., 28, 10971105, 2009.

Selby, M. J.: Hillslope Material and Processes, Oxford Univ. Press,

Oxford, UK, 451 pp., 1993.

Shaw, P. J. A.: Multivariate statistics for the Environmental

Sciences, Hodder-Arnold, ISBN 0-3408-0763-6, London, UK, 2003.

Smith, L. C., Alsdorf, D. E., Magilligan, F. J., Gomez, B., Mertes,L. A. K., Smith, N. D., and Garvin, J. B.: Estimation of ero-

Earth Surf. Dynam., 4, 489513, 2016 www.earth-surf-dynam.net/4/489/2016/

A. Loye et al.: Headwater sediment dynamics 513

sion, deposition and net volumetric change caused by the 1996 Skeidararsandur Jkulhlaup, Iceland, from SAR interferometry, Water Resour. Res., 36, 15831594, 2000.

Stark, C. P. and Hovius, N.: The characterization of landslide size distributions, Geophys. Res. Lett., 28, 10911094, 2001.

Stock, J. D. and Dietrich, W. E.,: Erosion of steepland valleys by debris ows, Geol. Soc. Am. Bull., 118, 11251148, 2006.Taylor, J. R.: An introduction to error analysis, University science

Books, Sausalito, California, USA, 2nd Edn., 327 pp., 1997.

Theule, J. I., Libault, F., Loye, A., Laigle, D., and Jaboyedoff, M.:

Sediment budget monitoring of debris-ow and bedload transport in the Manival Torrent, SE France, Nat. Hazards Earth Syst.Sci., 12, 731749, doi:http://dx.doi.org/10.5194/nhess-12-731-2012

Web End =10.5194/nhess-12-731-2012 http://dx.doi.org/10.5194/nhess-12-731-2012

Web End = , 2012.Theule, J. I., Libault, F., Laigle, D., Loye, A., and Jaboyedoff ,M.: Channel scour and ll by debris ows and bedload transport, Geomorphology, 243, 92105, 2015.

Van Dine, D. F.: Debris ow and debris torrents in the SouthernCanadian Cordillera, Can. Geotech. J., 22, 4468, 1985.

Van Steijn, H.: Debris-ow magnitude-frequency relationships for mountainous regions of Central and Northwest Europe, Geomorphology, 15, 259273, 1996.

Veyrat-Charvillon, S.: Elaboration dune mthode de prdiction du volume maximal dune lave torrentielle (PREVENT), Doctor Thesis, Universit Blaise Pascal, Clermont-Ferrant, France, 2, 354 pp., 2005.

Veyrat-Charvillon, S. and Memier, M.: Stereophotogrammetry of archive data and topographic approaches to debris-ow torrent measurements: Calculation of channel-sediment states and a partial sediment budget for Manival torrent (Isre, France), Earth Surf. Proc. Land., 31, 201219, 2006.

Wu, Y. and Cheng, H.: Monitoring of gully erosion on the Loess Plateau of China using a global positioning system, Catena, 63, 154166, 2005.

Zimmermann, M., Mani, P., and Romang, H.: Magnitudefrequency aspects of alpine debris ows, Eclogae Geol. Helv., 90, 415420, 1997.

www.earth-surf-dynam.net/4/489/2016/ Earth Surf. Dynam., 4, 489513, 2016

Word count: 14290

Show less

Abstract

Translate

Debris flows have been recognized to be linked to the amounts of material temporarily stored in torrent channels. Hence, sediment supply and storage changes from low-order channels of the Manival catchment, a small tributary valley with an active torrent system located exclusively in sedimentary rocks of the Chartreuse Massif (French Alps), were surveyed periodically for 16 months using terrestrial laser scanning (TLS) to study the coupling between sediment dynamics and torrent responses in terms of debris flow events, which occurred twice during the monitoring period. Sediment transfer in the main torrent was monitored with cross-section surveys. Sediment budgets were generated seasonally using sequential TLS data differencing and morphological extrapolations. Debris production depends strongly on rockfall occurring during the winter--early spring season, following a power law distribution for volumes of rockfall events above 0.1 m<sup>3</sup>, while hillslope sediment reworking dominates debris recharge in spring and autumn, which shows effective hillslope--channel coupling. The occurrence of both debris flow events that occurred during the monitoring was linked to recharge from previous debris pulses coming from the hillside and from bedload transfer. Headwater debris sources display an ambiguous behaviour in sediment transfer: low geomorphic activity occurred in the production zone, despite rainstorms inducing debris flows in the torrent; still, a general reactivation of sediment transport in headwater channels was observed in autumn without new debris supply, suggesting that the stored debris was not exhausted. The seasonal cycle of sediment yield seems to depend not only on debris supply and runoff (flow capacity) but also on geomorphic conditions that destabilize remnant debris stocks. This study shows that monitoring the changes within a torrent's in-channel storage and its debris supply can improve knowledge on recharge thresholds leading to debris flow.

Details

Title

Headwater sediment dynamics in a debris flow catchment constrained by high-resolution topographic surveys

Pages

489-513

Publication year

2016

Publication date

2016

Publisher

Copernicus GmbH

ISSN

21966311

e-ISSN

2196632X

Source type

Scholarly Journal

Language of publication

English

DOI

https://doi.org/10.5194/esurf-4-489-2016

ProQuest document ID

1803053456

Headwater sediment dynamics in a debris flow catchment constrained by high-resolution topographic surveys

Jump to:

Full text

Abstract

Details

Suggested sources